Large-scale graphs have become ubiquitous in many applications. Examples include review and co-purchase networks (e.g. Amazon, Yelp, …), protein interaction networks (e.g. BioGrid), or social networks (e.g. Facebook). Given such data, how to find groups of users showing similar behavior? How to spot fake reviews? How to predict which actions a user will likely perform tomorrow? Or how to find proteins showing surprising interactions? To answer these questions, automatic data analytics and machine learning principles are required.
The objective of this lab course (Master-Praktikum) is to develop data mining/machine learning algorithms specifically handling large graph data. Besides focusing on existing principles, the participants will also design and realize novel analysis techniques. The implemented techniques will be tested on multiple, large-scale graph datasets.
- First organizational meeting (Vorbesprechung): January 29th, 2016, 10:30am, room 00.13.054. All students who are interested in the lab course should step by.
- Weekly meetings during the semester: Mondays, 2pm-4pm, room 02.09.014
- The lab course is designed for Master students of Computer Science.
- Good knowledge in data mining/machine learning is a must (i.e. at least one of the related lectures "Mining Massive Datasets", "Machine Learning" etc.).
- Since the lab course focuses on the implementation of data mining/machine learning algorithms, strong programming skills (in C++, Java, or Python) are required.